A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Journal of Symbolic Computation
A New Way of Automating Statistical Testing Methods
Proceedings of the 16th IEEE international conference on Automated software engineering
A Generic Method for Statistical Testing
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Uniform random sampling of traces in very large models
Proceedings of the 1st international workshop on Random testing
Uniform random walks in very large models
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
On the Use of Uniform Random Generation of Automata for Testing
Electronic Notes in Theoretical Computer Science (ENTCS)
Uniform Monte-Carlo model checking
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
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This paper describes a set of methods for randomly drawing traces in large models either uniformly among all traces, or with a coverage criterion as target. Classical random walk methods have some drawbacks. In case of irregular topology of the underlying graph, uniform choice of the next state is far from being optimal from a coverage point of view. Moreover, for the same reason, it is generally not practicable to get an estimation of the coverage obtained after one or several random walks: it would require some complex global analysis of the model topology. We present here some methods that give up the uniform choice of the next state. These methods bias this choice according to the number of traces, or states, or transitions, reachable via each successor.